Do you truly know your customers’ expectations?

Social media is filled with stories where brands create unique experiences resulting in ‘customer delight'. While these brief moments are great, truly enduring customer loyalty grows and is sustained by delivering consistent experiences that align with customer expectations. To achieve this ideal state, brands must delve into understanding the complexity of customer expectations and inspire employees to exceed them. 

To best gauge customer expectations, brands have found success employing a personalized, non-intrusive feedback program that brings together the power of zero-party and first-party data. This approach not only supports journey orchestration and predictive analytics, but also empowers people across teams to intervene to turn experiences around in the moment.

Work through the following questions with your team:

Do you know which experiences matter most to your customers?

Are you capturing and analyzing your customers’ sentiments as they engage with your products, services, or experiences?

Do know whether your prospects and current customers have different expectations?

Do you have a balanced focus on customer acquisition, experience optimization, and retention or repurchase?

If you are considering a technology update or have recently added increasingly popular Customer Data Platforms (CDPs), having the answer to these questions can enable you to utilize its transformative power to mature. They are not just tools, but catalysts that enable businesses to effectively harness consumer data. When correctly implemented, CDPs can transition businesses from legacy customer experiences to ideal, real-time, behavior-driven interactions. They are the key to unlocking differentiated experiences that set businesses apart in today’s competitive landscape.

How can AI power your VoC programs?

VoC programs benefit significantly from artificial intelligence’s capabilities:

1. Rich data sources: While companies often deploy cutting-edge solutions, many times they overlook existing rich data sources. Customer data may be siloed across departments or remain unstructured. AI can unlock insights from these sources, saving time, resources, and money by reducing the need for a data infrastructure overhaul. 

2. Surveys: Surveys conducted through platforms like Qualtrics and Medallia provide valuable data attributes. AI-driven analysis can uncover meaningful patterns from website behavior, chat interactions, and call center data to help highlight and correct friction points.  

3. Move from one-for-all to 1-1: AI can help brands scale personalized interactions. By understanding the entire customer journey, businesses can move beyond the one-for-all approach to tailored experiences for a segment of individuals faster than what’s been possible before. 

4. Customer sentiment: AI deploys enhanced natural language processing (NLP) to analyze the sentiment of the open texts found in product reviews, social media interactions, and contact center transcripts. Sentiment analysis uncovers the myriad of customer emotions when interacting across journeys and at key touchpoints.

Intelligent Retargeting: The Abandon Cart Example

Consider the scenario of an abandoned cart. With AI, we can now intelligently retarget customers based on their sentiments and even set it to be automated. By analyzing audience behavior and emotions, businesses can craft personalized follow-up messages, enticing customers to complete their purchases more than the traditional generic abandon-cart emails.

Let’s illustrate how variations can be added to a journey that incorporates customer voices in the example of an abandon cart: 

Pre-purchase: Janet is interested in buying a treadmill and searches for one on Google. She clicks through to a treadmill retailer’s site. She is especially mindful of the treadmill specs and shipping costs. She adds the item to her cart.

During purchase: While the treadmill specs are exactly what she wants, Janet’s mouse movements indicate she is going to abandon her cart. She receives a prompt asking if she will provide feedback on the shopping/purchasing experience. Her feedback response indicates she is deterred by high shipping costs. With the help of AI, she receives an immediate response about the product specs including purchasing options with reduced or no shipping costs.

Because of this tag, Janet is placed into the retailer’s abandoned cart journey. With these notes pinpointing her interests, content is tailored to emphasize the product features that initially captivated her, while also suggesting a nearby physical store to mitigate shipping expenses. This is an example of how Next Best Experience (NBx) matures with AI’s capabilities. 

Post-purchase: The efforts paid off! Janet decides to purchase and pick up her new treadmill at a nearby location. The tailored journey moved Janet from an abandoned cart to a happy customer.

Want to get started with the voice of the customer programs or mature your programs and discuss ways to integrate with your existing martech investments?